Consumers have flocked to mobile devices for a range of applications. Popular applications include budgeting and banking applications. To use these applications, a consumer will, for example, take a photo of a paper document that is a receipt or a check. The mobile device then performs some type of optical character recognition on the document, turning the raw image into alphanumeric character data for storage.
Despite some success, consumers are often frustrated by the inaccuracy of the optical character recognition (OCR) process. There are at least several reasons for these inaccuracies. Unlike large, fixed scanners, handheld electronic devices struggle to capture good images for OCR processing. For example, handheld mobile (and other electronic) devices are prone to unsteady and imperfect photographing of the document. In addition, lighting and backgrounds can vary introducing artefacts and/or affecting the amount of contrast in the image. A handheld device can also suffer from skew introduced by not having the camera's focal plane square with the document itself.
Other challenges are introduced by the documents themselves. Documents have differing characteristics, such as varying fonts, and the OCR process can fail to interpret various stylistic font differences. Varied documents also have varied sizes—leading many banking applications to focus just on checks having a predictable size.
Current applications focus on a mixture of guiding the consumer to take better images and image processing in an attempt to improve accuracy. For example, some banking applications provide the consumer a frame in which to position the check to avoid skew and improve the resolution of the check image. These applications may also reject a check that is insufficiently clear. Conventional image processing can include binarization to remove background artefacts. However, digital cameras auto-focus on the image as a whole, rather than on individual objects such as fonts. In the context of acquiring images or receipts, checks, etc. for extraction of data for OCR processing, this leads to images containing blurry fonts. Blurry text fonts in the source image result in substandard OCR results. It is therefore desirable to improve the accuracy and efficiency of image capture and OCR processing of documents, especially documents captured using handheld electronic devices.